Information architecture (IA), or semantic infrastructure, can be seen as a blueprint defining how information is standardized, structured and organized. IA is an attempt to organize information in a way that it is findable, manageable and useful. In particular, it is a framework that assesses, describes, and connects organizational information to its business process. Its objective is to identify and leverage patterns in data by making complex sets of information easier to exploit. By having an IA, organizations can better access, share, and consolidate their information holdings to support the needs of business processes and their management. However, in many organizations, the IA is often poorly designed or absent altogether.
Within an organization, several information management (IM) tools are often used independently from each other within an organization, to satisfy specific IM needs. Typically, file classification, taxonomies, metadata, thesauri and data models (i.e., data structures required by a database) each use their own independent databases to store data in various formats, with these databases not interacting with each other. The resulting isolated functionality often leads to a set of inconsistent, overlapping, and incompatible information systems that are difficult to maintain and often results in the creation of silos where information becomes stranded from potentially valuable organizational uses.
The use of multiple IM tools within an organization often leads to the coexistence of several standards restricted to specific IM needs. In some cases, groups within the organization develop their own standardization procedures or do not use a standard whatsoever. The absence of uniform and comprehensive IM standards creates an environment where information is inconsistent, difficult to access, and unreliable. Further, the lack of such standards often leads to the duplication of information to meet requirements of several different IM tools.
An information object (IO), or information asset, is defined as an object that has importance to an organization. Examples of IOs include all types of documents produced by software. Traditional IM tools do not afford rich descriptions regarding the aboutness of information objects. Using keywords, for instance, will not necessarily provide reliable descriptions because the interpretation of the terms used can often have various meanings in different contexts. Furthermore, organizational documents often discuss a specific topic without ever referring to it explicitly. This can lead to additional descriptive irregularities.
To add to the disorder, end-users are increasingly being required to manage their information holdings by performing specific IM tasks over and above their work related tasks. This often leads to poor metadata being attributed to IOs, which leads to user frustration, error and reduced productivity.
Additionally, in many cases, corporate IA is designed to meet the requirements of a specific software solution and, as a consequence, lacks the necessary flexibility to adapt to frequent technological changes.
It is, therefore, desirable to provide a more comprehensive and better-structured description of document content in order to allow a more effective and broad-ranged use of an organization's information objects while also making them easier to retrieve.
It is also desirable, within an IA, to have classification and metadata generation procedures that are intelligent in that they streamline a user's IM tasks while ensuring that information is accurately and efficiently managed.
Additionally, it is desirable to provide an IA that is independent of specific software solutions and allows the various technical systems to leverage benefits from the IA.